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Heterogeneous Sensor Fusion via Confidence-Rich 3D Grid Mapping: Application to Physical Robots

Heiden, Eric and Pastor, Daniel and Vyshnav, Pradyumna and Agha-Mohammadi, Ali-Akbar (2020) Heterogeneous Sensor Fusion via Confidence-Rich 3D Grid Mapping: Application to Physical Robots. In: Proceedings of the 2018 International Symposium on Experimental Robotics. Springer Proceedings in Advanced Robotics. No.11. Springer , Cham, pp. 725-736. ISBN 978-3-030-33949-4. https://resolver.caltech.edu/CaltechAUTHORS:20200123-102509372

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Abstract

Autonomous navigation of intelligent physical systems largely depend on the ability of the system to generate an accurate map of its environment. Confidence-rich grid mapping algorithm provides a novel representation of the map based on range data by storing richer information at each voxel, including an estimate of the variance of occupancy. Capabilities and limitations are attributes of any given sensor, and therefore a single sensor may not be effective in providing detailed assessment of dynamic terrains. By incorporating multiple sensory modalities in a robot and extracting fused sensor information from them leads to higher certainty, noise reduction, and improved failure tolerance when mapping in real-world scenarios. In this work we investigate and evaluate sensor fusion techniques using confidence-rich grid mapping through a series of experiments on physical robotic systems with measurements from heterogeneous ranging sensors.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
https://doi.org/10.1007/978-3-030-33950-0_62DOIArticle
Additional Information:© 2020 Springer Nature Switzerland AG. First Online: 23 January 2020. This research was carried out at the Jet Propulsion Laboratory under a contract with the National Aeronautics and Space Administration. U.S. Government sponsorship acknowledged.
Group:GALCIT
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Funding AgencyGrant Number
NASA/JPLUNSPECIFIED
Series Name:Springer Proceedings in Advanced Robotics
Issue or Number:11
Record Number:CaltechAUTHORS:20200123-102509372
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20200123-102509372
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:100874
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:23 Jan 2020 18:43
Last Modified:23 Jan 2020 18:43

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